Preventive planning model for rescue priority management in seismic emergency

Natural materials, such as soils, are influenced by many factors acting during their formative and evolutionary process: atmospheric agents, erosion and transport phenomena, sedimentation conditions that give soil properties a non-reducible randomness by using sophisticated survey techniques and technologies. This character is reflected not only in spatial variability of properties which differs from point to point, but also in multivariate correlation as a function of reciprocal distance. Cognitive enrichment, offered by the response of soils associated with their intrinsic spatial variability, implies an increase in the evaluative capacity of the contributing causes and potential effects in failure phenomena. Stability analysis of natural slopes is well suited to stochastic treatment of uncertainty which characterized landslide risk. In particular, this study has been applied through a back- analysis procedure to a slope located in Southern Italy that was subject to repeated phenomena of hydrogeological instability (extended for several kilometres in recent years). The back-analysis has been carried out by applying spatial analysis to the controlling factors as well as quantifying the hydrogeological hazard through unbiased estimators. A natural phenomenon, defined as stochastic process characterized by mutually interacting spatial variables, has led to identify the most critical areas, giving reliability to the scenarios and improving the forecasting content. Moreover, the phenomenological characterization allows the optimization of the risk levels to the wide territory involved, supporting decision-making process for intervention priorities as well as the effective allocation of the available resources in social, environmental and economic contexts

Empirical Scenarios of Fake Data Analysis: The Sample Generation by Replacement (SGR) Approach

Many self-report measures of attitudes, beliefs, personality, and pathology include items whose responses can be easily manipulated or distorted, as an example in order to give a positive impression to others, to obtain financial compensation, to avoid being charged with a crime, to get a job, or else. This fact confronts both researchers and practitioners with the crucial problem of biases yielded by the usage of standard statistical models. The current paper presents three empirical applications to the issue of faking of a recent probabilistic perturbation procedure called Sample Generation by Replacement (SGR; Lombardi and Pastore, 2012). With the intent to study the behavior of some statistics under fake perturbation and data reconstruction processes, ad-hoc faking scenarios were implemented and tested. Overall, results proved that SGR could be successfully applied both in the case of research designs traditionally proposed in order to deal with faking (e.g., use of fake-detecting scales, experimentally induced faking, or contrasting applicants vs. incumbents), and in the case of ecological research settings, where no information as regards faking could be collected by the researcher or the practitioner. Implications and limitations are presented and discussed

Letter by Aquaro et al Regarding Article, "Intermediate-Signal-Intensity Late Gadolinium Enhancement Predicts Ventricular Tachyarrhythmias in Patients With Hypertrophic Cardiomyopathy"

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Observations of Extrasolar Planets During the non-Cryogenic Spitzer Space Telescope Mission

Precision infrared photometry from Spitzer has enabled the first direct studies of light from extrasolar planets, via observations at secondary eclipse in transiting systems. Current Spitzer results include the first longitudinal temperature map of an extrasolar planet, and the first spectra of their atmospheres. Spitzer has also measured a temperature and precise radius for the first transiting Neptune-sized exoplanet, and is beginning to make precise transit timing measurements to infer the existence of unseen low mass planets. The lack of stellar limb darkening in the infrared facilitates precise radius and transit timing measurements of transiting planets. Warm Spitzer will be capable of a precise radius measurement for Earth-sized planets transiting nearby M-dwarfs, thereby constraining their bulk composition. It will continue to measure thermal emission at secondary eclipse for transiting hot Jupiters, and be able to distinguish between planets having broad band emission versus absorption spectra. It will also be able to measure the orbital phase variation of thermal emission for close-in planets, even non-transiting planets, and these measurements will be of special interest for planets in eccentric orbits. Warm Spitzer will be a significant complement to Kepler, particularly as regards transit timing in the Kepler field. In addition to studying close-in planets, Warm Spitzer will have significant application in sensitive imaging searches for young planets at relatively large angular separations from their parent stars.Comment: 12 pages, 7 figures, to appear in "Science Opportunities for the Warm Spitzer Mission

The Efficiency of Waste Sector in Italy: An Application by Data Envelopment Analysis

With growing environmental legislation and mounting popular concern for the need to pursuing a sustainable growth, there has been an increasing recognition in developed nations of the importance of waste reduction, recycling and reuse maximization. This empirical study investigates both ecological and economic performances of urban waste systems in 78 major Italian towns for the years 2015 and 2016. To this purpose the study employs the non-parametric approach to efficiency measurement, represented by Data Envelopment Analysis (DEA) technique. More specifically, in the context of environmental performance we implement two output-oriented DEA models in order to consider both constant and variable returns to scale. In addition, we include an undesirable output – the total amount of waste collected – in the two models considered. The results show that there is variability among the municipalities analysed: Northern and Central major towns show higher efficiency scores than Southern and Islands ones

Geostatistical modeling of seismic actions on the structural components of the San Benedetto road tunnel, Italy

The Italian legislation requires determining the project seismic actions and to carry out the dynamic stability verification of the structural elements of a building or road infrastructure on the base of the seismic hazard curve of the construction site. The geographic density of the available seismic data requires the best use of existing data and above all not introducing phenomenological assumptions and unverified information into the survey. This article proposes an investigation protocol that integrates the multivariate statistical analysis methodologies already used to determine the seismic shaking attenuation with more efficient and versatile geostatistical methodologies for a more realistic estimate of non-stationary parameters. The application of the new investigation protocol to the earthquakes that occurred in Italy in 2016 made it possible to detect and resolve three fundamental aspects of seismic modeling: (i) the recorded data highlight the presence of a directional anisotropy of the arrival time and the value of the acceleration peak on the ground which led to the introduction of a non-Euclidean metric in seismic modeling; (ii) the presence and measurement of the geographical continuity of the irregular variations between data pairs attributable to the heterogeneity of the rock formations and outcropping soils; (iii) the need for an experimental measure of the estimate uncertainty for an objective evaluation of the applied numerical estimator. This last result made it possible to evaluate the gain in terms of accuracy of the estimate performed with the local geostatistical estimator. The article presents the estimate of the arrival time and the peak value of the vertical (PGAV) and maximum horizontal (PGAH) component of the acceleration of the vibrational movements of the rock around the San Benedetto tunnel during the Norcia earthquake. The maximum estimated values of PGAV and PGAH are around the section of the tunnel damaged by the earthquake

Risk analysis and reliability of the GERDA Experiment extraction and ventilation plant at Gran Sasso mountain underground laboratory of Italian National Institute for Nuclear Physics

The aim of this study is the risk analysis evaluation about argon release from the GERDA experiment in the Gran Sasso underground National Laboratories (LNGS) of the Italian National Institute for Nuclear Physics (INFN). The GERDA apparatus, located in Hall A of the LNGS, is a facility with germanium detectors located in a wide tank filled with about 70 m3 of cold liquefied argon. This cryo-tank sits in another water-filled tank (700 m3) at atmospheric pressure. In such cryogenic processes, the main cause of an accidental scenario is lacking insulation of the cryo-tank. A preliminary HazOp analysis has been carried out on the whole system. The risk assessment identified two possible top-events: explosion due to a Rapid Phase Transition - RPT and argon runaway evaporation. Risk analysis highlighted a higher probability of occurrence of the latter top event. To avoid emission in Hall A, the HazOp, Fault Tree and Event tree analyses of the cryogenic gas extraction and ventilation plant have been made. The failures related to the ventilation system are the main cause responsible for the occurrence. To improve the system reliability some corrective actions were proposed: the use of UPS and the upgrade of damper opening devices. Furthermore, the Human Reliability Analysis identified some operating and management improvements: action procedure optimization, alert warnings and staff training. The proposed model integrates the existing analysis techniques by applying the results to an atypical work environment and there are useful suggestions for improving the system reliability

Risk based tunnel design by vehicle involved in road crashes. Models and tunnel length

Between 2018 and 2020, in the approximately 2,600 km of Italian road tunnels, 2,899 people were injured and 60 died in 1,885 road accidents. The accident frequency was lower than on open roads, while the injury/fatality rate was higher. Using the recursive partitioning and regression trees method (rpart), we developed two accident models useful for predicting the probability of involvement of "vehicle type" in short and long tunnels. Variables such as the type of accident, the circumstances, the type of road, the carriageway, the time of the accident, the journey purpose (whether work-related or not), and the length of the tunnel defined the nodes and paths of the regression tree associated with a vehicle type involved. The “road type” was the best predictors for short tunnels while the “journey purpose” was the best predictor for long tunnels. The most important result of the study refers to the similarity between the probability of an accident in short and long tunnels for a specific segment of road users: commuting and non-commuting car drivers and drivers of heavy goods vehicles on-duty. The study showed that this road user segment in short tunnels has an accident probability half that observed in long tunnels

Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. Here, we investigate perceptions of math and STEM fields provided by cutting-edge language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach from network science and cognitive psychology. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have an overall negative perception of math and STEM fields, with math being perceived most negatively. We observe significant differences across the three LLMs. We observe that newer versions (i.e. GPT-4) produce richer, more complex perceptions as well as less negative perceptions compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.Comment: 23 pages, 8 figure

A Context-Aware Mobile Solution for Assisting Tourists in a Smart Environment

The cultural heritage of the italian territory is an impressive breadth of resources, unfortunately still little known today. Such a cultural heritage should be valued for the purpose is to better understand the Italian citizens their cultural identity, is to make known to the citizens of other nations the history of the place, with its story and its characters, and the life of the inhabitants, with their own traditions and customs. \ \ In this paper, it is introduced an adaptive Context-Aware app able to collect not-structured data, belonging to heterogeneous sources and develop tailored recommendations for the user, in order to support a tourist inside a town. The solution found takes advantage of information technologies, like Internet of Thing and Internet of Services and the objective is reached through the use of a system of description of the context through a graphical formalism named Context Dimension Tree. \ \ The system described was implemented in the city of Salerno in Italy and the results of a questionnaire distributed to the users show great appreciation